Traffic signs are one of the most important assets for transportation systems. They provide vital guidance to road users regarding traffic regulation, warnings, destination information, and temporary road condition information. Because of the vital role traffic signs play in roadway safety and information conveyance, they must be managed effectively by state departments of transportation (DOTs) using a traffic sign management system. A traffic sign management system is a coordinated program of policies and procedures that ensure that the highway agency provides a sign system that meets the needs of the user most cost-effectively within available budget and constraints. It contains the four primary components. The first component is to inventory traffic signs, which collects the locations and attributes of every individual traffic sign. The second component is condition assessment, which determines the performance adequacy of inventoried signs by assessing retroreflectivity and identifying visual defects. The third component is performance evaluation, which evaluates a traffic sign system's performance and predicts the performance and life of an individual sign or a group of signs in the system based on the collected condition data. The fourth component is decision-making, which generates maintenance strategies, methods, and prioritizations based on the performance evaluation outcomes and available budget, and generates the needed annual budget based on expected safety requirements. Traffic sign inventory and condition assessment are the two most important components in a traffic sign management system. Management actions can only be effectively carried out with reliable inventory and condition assessment information.
During traffic sign inventory, detailed traffic sign information, including sign locations and attributes (e.g. type, dimension, lateral offset, etc.), is recorded and used to build a comprehensive traffic sign inventory database. Among all traffic sign information in a sign inventory database, identifying where the traffic signs are (i.e. traffic sign detection) is the first and most critical step, without which all the remaining sign attribute information cannot be acquired or populated in the database. However, most transportation agencies do not even have the information about where their traffic signs are along the road, not to mention the detailed traffic sign attribute information. There is a need to develop methods to cost-effectively and reliably locate traffic signs first so that the remaining detailed traffic sign information can be populated and the subsequent management operations can be successfully carried out, e.g. condition assessment, performance evaluation, etc.
During traffic sign condition assessment, the traffic signs that do not meet requirements are identified by insufficient retroreflectivity and/or visual surface defects that interfere with traffic signs' displayed information. Among all the traffic sign conditions, traffic sign retroreflectivity condition is the most critical one for nighttime driving safety. There is an urgent need to develop methods to cost-effectively and reliably evaluate traffic sign retroreflectivity condition.
Traditionally, traffic sign detection and retroreflectivity condition assessments have used manual methods in state DOTs. However, manual methods require field engineers to physically inspect and record the information of each individual traffic sign, which takes excessive time, consumes great amounts of labor, and sometimes puts field engineers in dangerous situations. To overcome the drawbacks of manual methods, some effort has been made to develop automated methods for both traffic sign detection and retroreflectivity condition assessment using video log images. However, the performance of these methods still needs to be improved so that they can be practically applied in state DOTs' practices. In recent years, emerging sensing technologies, e.g. computer vision, mobile light detection and ranging (LiDAR), etc. have advanced greatly so that current image-based automatic methods have been improved, and new traffic sign detection and retroreflectivity condition assessment methods have become possible. Consequently, this study focuses on the two key needs in the inventory and condition assessment components of a traffic sign management system: 1) developing an enhanced traffic sign detection methodology to improve the productivity of an image-based sign inventory for state DOTs, and 2) exploring and developing a new traffic sign retroreflectivity condition assessment methodology to cost-effectively and reliably assess traffic sign retroreflectivity conditions using the emerging computer vision and mobile LiDAR technologies.
Retroreflectivity is a critical attribute of a traffic sign for nighttime visibility. It can be defined as the ratio of the luminance that is redirected from a sign's surface to the luminance originating from a vehicle's headlight (ASTM, 2011). A LiDAR system can collect the retro-intensity values in a way similar to the measurement of traffic sign retroreflectivity. A retro-intensity value can be acquired with each LiDAR point. A retro-intensity value represents the ratio of the energy redirected from the object to the energy emitted from the LiDAR sensor. Thus, the retro-intensity values can be correlated with traffic sign retroreflectivity conditions. Such a correlation can potentially be used to conduct an automatic traffic sign retroreflectivity condition assessment.
In doing so, it may be advantageous to provide systems and methods that can associate color data of the traffic sign with the raw LiDAR point cloud data so that multiple colors for the same traffic sign may be assessed separately as to whether they meet the manual of uniform traffic control devices (MUTCD) requirements. Furthermore, it may also be advantageous to provide systems and methods that can assess the population of the retro-intensity values associated with the same traffic sign at the same beam distance and incidence angle, despite differences in beam distances and incidence angles that arise during raw retro-intensity value acquisition.